Designing a Fault-Tolerant System for Hearing Aids: Echo Detection and Rectification Using SVM Classifier

Abstract

Hearing aids, akin to various mechanical instruments, emit specific auditory signals indicative of operational health, accurate parameter settings, or maintenance requirements. Analogous to automobile users discerning vehicle health via auditory cues without formal instruction, such auditory recognition emerges rapidly. Yet, embodying this auditory discernment within an artificial framework introduces intricate challenges. The scope of artificial structures capable of pre-emptive defect or maintenance identification through auditory signals remains expansive. In hearing aids, commonly observed complications encompass inadequate amplification, acoustic reverberations presenting as echoes, and diminished acoustic integrity leading to auditory deformities, frequently labelled as Non-linear harmonic distortion. The present study predominantly concentrates on the pervasive issue of auditory reverberations in hearing aids, often leading to user discomfort. The primary goal revolves around developing a resilient system harnessing an SVM classifier to detect echoes. Notably, the outlined architecture has showcased an accuracy metric of 95.4%.